Qwen3 30B-A3B Instruct 2507 vs Llama 4 Scout 17B Instruct
Too close to call on our weighted score (Llama 4 Scout 17B Instruct 61, Qwen3 30B-A3B Instruct 2507 59). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3 30B-A3B Instruct 2507
59/100- ECI137.3
- Price$0.10 / $0.30
- Context262K
MetaLlama 4 Scout 17B Instruct
61/100- ECI129.6
- Price$0.17 / $0.59
- Context10M
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Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 2 points (Llama 4 Scout 17B Instruct 61/100, Qwen3 30B-A3B Instruct 2507 59/100), so choose by what matters most for your work: Qwen3 30B-A3B Instruct 2507 for raw capability and Llama 4 Scout 17B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3 30B-A3B Instruct 2507Capabilities Index (ECI): Qwen3 30B-A3B Instruct 2507 137.3 · Llama 4 Scout 17B Instruct 129.6
- Lowest priceQwen3 30B-A3B Instruct 2507Qwen3 30B-A3B Instruct 2507 $0.15 · Llama 4 Scout 17B Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct 10,000,000 · Qwen3 30B-A3B Instruct 2507 262,144 tokens
- Widest inputsLlama 4 Scout 17B InstructQwen3 30B-A3B Instruct 2507: Text · Llama 4 Scout 17B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 30B-A3B Instruct 2507 | Llama 4 Scout 17B Instruct |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 61 | 51 |
| Price | 25% | 85 | 73 |
| Inputs & features | 15% | 25 | 50 |
| Context window | 10% | 37 | 100 |
| Overall | 100% | 59/100 | 61/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3 30B-A3B Instruct 2507Alibaba (Qwen) | Llama 4 Scout 17B InstructMeta |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 137.3 (best) | 129.6 |
| ECI rank | #124 of 165 (best) | #138 of 165 |
| GPQA DiamondGraduate-level science questions | 55.6% (best) | 51.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 62.2% (best) | 7.8% |
| Price per million tokens | ||
| Input | $0.10 (best) | $0.17 |
| Output | $0.30 (best) | $0.59 |
| Cached input | — | — |
| Blended (3:1) | $0.15 (best) | $0.275 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 12 providers | Median of 13 providers |
| Limits | ||
| Context window | 262,144 tokens | 10,000,000 tokens (best) |
| Max output | 16,384 tokens | 16,384 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | No |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenApache 2.0 | Open |
| API model ID | — | — |
| API providers | 14 (best) | 13 |
| Released | Jul 28, 2025 | Apr 5, 2025 |
| Knowledge cutoff | — | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3 30B-A3B Instruct 2507$1.60
- Llama 4 Scout 17B Instruct$2.88
Which should you choose?
Which is better: Qwen3 30B-A3B Instruct 2507 or Llama 4 Scout 17B Instruct?
It is close. Our weighted score puts them within 2 points (Llama 4 Scout 17B Instruct 61/100, Qwen3 30B-A3B Instruct 2507 59/100), so choose by what matters most for your work: Qwen3 30B-A3B Instruct 2507 for raw capability and Llama 4 Scout 17B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3 30B-A3B Instruct 2507 or Llama 4 Scout 17B Instruct?
Qwen3 30B-A3B Instruct 2507 is cheaper at $0.10 input / $0.30 output per million tokens (median across 12 API providers). Llama 4 Scout 17B Instruct costs $0.17 input / $0.59 output per million tokens (median across 13 API providers). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Qwen3 30B-A3B Instruct 2507 versus $0.275 for Llama 4 Scout 17B Instruct (1.8× as much).
Which scores higher on benchmarks?
Qwen3 30B-A3B Instruct 2507 scores higher on the Capabilities Index (ECI): Qwen3 30B-A3B Instruct 2507 137.3 (#124 of 165) and Llama 4 Scout 17B Instruct 129.6 (#138 of 165). Their confidence ranges do not overlap (131.7–139.4 vs 125.2–131.2), so the gap is a real one. On individual benchmarks: GPQA Diamond — Qwen3 30B-A3B Instruct 2507 55.6%, Llama 4 Scout 17B Instruct 51.8%; OTIS Mock AIME 2024–2025 — Qwen3 30B-A3B Instruct 2507 62.2%, Llama 4 Scout 17B Instruct 7.8%.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 30B-A3B Instruct 2507 and Llama 4 Scout 17B Instruct yet, so there is no like-for-like coding score. On overall capability, Qwen3 30B-A3B Instruct 2507 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
Llama 4 Scout 17B Instruct has the largest context window at 10,000,000 tokens, against 262,144 for Qwen3 30B-A3B Instruct 2507. Maximum output per response: Qwen3 30B-A3B Instruct 2507 up to 16,384, Llama 4 Scout 17B Instruct up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Qwen3 30B-A3B Instruct 2507 accepts text; Llama 4 Scout 17B Instruct accepts text and images. Llama 4 Scout 17B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Qwen3 30B-A3B Instruct 2507 is the newest, released Jul 28, 2025. Llama 4 Scout 17B Instruct came out Apr 5, 2025. Knowledge cutoff: Llama 4 Scout 17B Instruct Aug 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
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